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Dementia, post-traumatic stress disorder and major depressive disorder: a review of the mental health risk factors for dementia in the military veteran population.

ABSTRACT: Dementia is currently incurable, irreversible and a major cause of disability for the world's older population. The association between mental health difficulties, such as post-traumatic stress disorder (PTSD) and major depressive disorder (MDD), and dementia has a long history within the civilian population. Despite the increased importance of this link within the military veteran population, who suffer a greater propensity of mental health difficulties and consist largely of over 65s, attention is only recently being paid to the salience of such an association for this group. This paper aims to explore the relationship between PTSD and MDD with dementia within the military veteran population.A systematic review was conducted on articles from 1990 to July 2016 on MEDLINE, EMBASE, EBSCO and Web of Science electronic databases with an update conducted in February 2017.Six empirical studies were identified from the review, the majority of which originated from the USA. Five of the studies asserted that veterans with a diagnosis of either PTSD or MDD are at a significantly greater risk of developing dementia than 'healthy' controls. The final study, conducted in Australia, found only a small, but non-significant, correlation between earlier MDD and future dementia, but no concurrent correlation.While causality cannot be determined, it is likely that PTSD and depressive disorders are related to an increased risk of dementia in military veterans. Potential pathological explanations and risk factors are reviewed and the clinical and neuroscience implications of these findings are explored.

SUBMITTER: Rafferty LA

PROVIDER: S-EPMC6088525 | biostudies-other | 2018 Jul

REPOSITORIES: biostudies-other

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Dementia, post-traumatic stress disorder and major depressive disorder: a review of the mental health risk factors for dementia in the military veteran population.

Rafferty L A LA Cawkill P E PE Stevelink S A M SAM Greenberg K K Greenberg N N

Psychological medicine 20180308 9

<h4>Background</h4>Dementia is currently incurable, irreversible and a major cause of disability for the world's older population. The association between mental health difficulties, such as post-traumatic stress disorder (PTSD) and major depressive disorder (MDD), and dementia has a long history within the civilian population. Despite the increased importance of this link within the military veteran population, who suffer a greater propensity of mental health difficulties and consist largely of ...[more]

PMID: 29514722

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Project description:BackgroundFirefighters, as first responders with a high risk of occupational exposure to traumatic events and heavy working stress, have a high prevalence of PTSD symptoms and depressive symptoms. But no previous studies analyzed the relationships and hierarchies of PTSD and depressive symptoms among firefighters. Network analysis is a novel and effective method for investigating the complex interactions of mental disorders at the symptom level and providing a new understanding of psychopathology. The current study was designed to characterize the PTSD and depressive symptoms network structure in the Chinese firefighters.MethodThe Primary Care PTSD Screen for DSM-5 (PC-PTSD-5) and the Self-Rating Depression Scale (SDS) were applied to assess PTSD and depressive symptoms, respectively. The network structure of PTSD and depressive symptoms was characterized using "expected influence (EI)" and "bridge EI" as centrality indices. The Walktrap algorithm was conducted to identify communities in the PTSD and depressive symptoms network. Finally, Network accuracy and stability were examined using the Bootstrapped test and the case-dropping procedure.ResultsA total of 1,768 firefighters were enrolled in our research. Network analysis revealed that the relationship between PTSD symptoms, "Flashback" and "Avoidance," was the strongest. "Life emptiness" was the most central symptom with the highest EI in the PTSD and depression network model. Followed by "Fatigue" and "Interest loss." Bridge symptoms connecting PTSD and depressive symptoms in our study were "Numb," "High alertness," "Sad mood," and "Compunction and blame," successively. The data-driven community detection suggested the differences in PTSD symptoms in the clustering process. The reliability of the network was approved by both stability and accuracy tests.ConclusionTo the best of our knowledge, the current study first demonstrated the network structure of PTSD and depressive symptoms among Chinese firefighters, identifying the central and bridge symptoms. Targeting interventions to the symptoms mentioned above may effectively treat firefighters suffering from PTSD and depressive symptoms.

Project description:Depressive symptoms are related to abnormalities in the autonomic nervous system (ANS), and physiological signals that can be used to measure and evaluate such abnormalities have previously been used as indicators for diagnosing mental disorder, such as major depressive disorder (MDD). In this study, we investigate the feasibility of developing an objective measure of depressive symptoms that is based on examining physiological abnormalities in individuals when they are experiencing mental stress. To perform this, we recruited 30 patients with MDD and 31 healthy controls. Then, skin conductance (SC) was measured during five 5-min experimental phases, comprising baseline, mental stress, recovery from the stress, relaxation, and recovery from the relaxation, respectively. For each phase, the mean amplitude of the skin conductance level (MSCL), standard deviations of the SCL (SDSCL), slope of the SCL (SSCL), mean amplitude of the non-specific skin conductance responses (MSCR), number of non-specific skin conductance responses (NSCR), and power spectral density (PSD) were evaluated from the SC signals, producing 30 parameters overall (six features for each phase). These features were used as input data for a support vector machine (SVM) algorithm designed to distinguish MDD patients from healthy controls based on their physiological responses. Statistical tests showed that the main effect of task was significant in all SC features, and the main effect of group was significant in MSCL, SDSCL, SSCL, and PSD. In addition, the proposed algorithm achieved 70% accuracy, 70% sensitivity, 71% specificity, 70% positive predictive value, 71% negative predictive value in classifying MDD patients and healthy controls. These results demonstrated that it is possible to extract meaningful features that reflect changes in ANS responses to various stimuli. Using these features, detection of MDD was feasible, suggesting that SC analysis has great potential for future diagnostics and prediction of depression based on objective interpretation of depressive states.

Project description:Background: Major Depressive Disorder (MDD) is a moderately heritable disorder with a high lifetime prevalence. At present, laboratory blood tests to support MDD diagnosis are not available. Methods: We used a classifier approach on blood gene expression profiles of a unique set of non-medicated subjects (MDD patients and controls) to select genes of which expression is predictive for disease status. To reveal blood gene expression changes related to MDD disease, we applied a powerful ex vivo stimulus to the blood, i.e. incubation with lipopolysaccharide (LPS; 10 ng/ml blood). Results: Based on LPS-stimulated blood gene expression using whole-genome microarrays in 42 subjects (primary cohort; 21 MDD patients (mean age 42.3 years), 21 healthy controls (mean age 41.9 years)), we identified a set of genes (CAPRIN1, CLEC4A, KRT23, MLC1, PLSCR1, PROK2, ZBTB16) that serves as a molecular signature of MDD. These findings were validated for the primary cohort using an independent quantitative PCR method (P = 0.007). The difference between depressive patients and controls was confirmed (P = 0.019) in a replication cohort of 13 patients with MDD (mean age 42.8 years) and 14 controls (mean age 45.6 years). The MDD-signature score comprised of expression levels of 7 genes could discriminate depressive patients from controls with sensitivity of 76.9% and specificity of 71.8%. Conclusions: We show for the first time that molecular analysis of stimulated blood cells can be used as an endophenotype for MDD diagnosis, which is a milestone in establishing biomarkers for neuropsychiatric disorders with moderate heritability in general. Our results may provide a new entry point for following and predicting treatment outcome, as well as prediction of severity and recurrence of MDD. In total, 33 MDD patients and 34 healthy controls were analyzed using basal gene expression in whole blood, and gene expression from whole blood that was stimulated with LPS for 5-6 h, using microarrays. Patients were arbitrarily selected from all patients to serve as primary cohort (nMDD = 21 (MDD01-MDD21); nControls = 21 (Con01-Con21)), or replication cohort (nMDD = 12 (MDD22-MDD35); nControls = 13 (Con22-Con37)) using microarrays. This submission does not include Samples CON21_LPS or CON30_LPS.

Dataset Information

Dementia, post-traumatic stress disorder and major depressive disorder: a review of the mental health risk factors for dementia in the military veteran population.

Publications

Dementia, post-traumatic stress disorder and major depressive disorder: a review of the mental health risk factors for dementia in the military veteran population.

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